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Your search keyword '"Islam, Md. Adnanul"' showing total 16 results
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16 results on '"Islam, Md. Adnanul"'

1. MosquitoMiner: A Light Weight Rover for Detecting and Eliminating Mosquito Breeding Sites

Author

Islam, Md. Adnanul, Sayeedi, Md. Faiyaz Abdullah, Deepti, Jannatul Ferdous, Bappy, Shahanur Rahman, Islam, Safrin Sanzida, and Hafiz, Fahim

Subjects
Computer Science - Robotics
Abstract

In this paper, we present a novel approach to the development and deployment of an autonomous mosquito breeding place detector rover with the object and obstacle detection capabilities to control mosquitoes. Mosquito-borne diseases continue to pose significant health threats globally, with conventional control methods proving slow and inefficient. Amidst rising concerns over the rapid spread of these diseases, there is an urgent need for innovative and efficient strategies to manage mosquito populations and prevent disease transmission. To mitigate the limitations of manual labor and traditional methods, our rover employs autonomous control strategies. Leveraging our own custom dataset, the rover can autonomously navigate along a pre-defined path, identifying and mitigating potential breeding grounds with precision. It then proceeds to eliminate these breeding grounds by spraying a chemical agent, effectively eradicating mosquito habitats. Our project demonstrates the effectiveness that is absent in traditional ways of controlling and safeguarding public health. The code for this project is available on GitHub at - https://github.com/faiyazabdullah/MosquitoMiner, Comment: Accepted - 2024 IEEE Region 10 Symposium (TENSYMP 2024)

Published
2024

3. Predicting Users’ Eat-Out Preference from Big5 Personality Traits

Author

Mukta, Md. Saddam Hossain, Zaman, Akib, Islam, Md. Adnanul, Hossain, Bayzid Ashik, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kumar, Sandeep, editor, Sharma, Harish, editor, Balachandran, K., editor, Kim, Joong Hoon, editor, and Bansal, Jagdish Chand, editor

Published
2023
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4. Exploring Word2vec Embedding for Sentiment Analysis of Bangla Raw and Romanized Text

Author

Zim, Sumaiya Kashmin, Ashraf, Fardeen, Iqbal, Tasnia, Islam, Md. Adnanul, Polok, Isbat Khan, Ahmed, Lopa, Rahman, Md. Mahbubur, Mukta, Md. Saddam Hossain, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Saraswat, Mukesh, editor, Chowdhury, Chandreyee, editor, Kumar Mandal, Chintan, editor, and Gandomi, Amir H., editor

Published
2023
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11. Punctuation Prediction in Bangla Text

Author

Rahman, Habibur, primary, Rahin, Md. Rezwan Shahrior, additional, Mahbub, Araf Mohammad, additional, Islam, Md. Adnanul, additional, Mukta, Md. Saddam Hossain, additional, and Rahman, Md. Mahbubur, additional

Published
2023
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14. Data driven classification of opioid patients using machine learning - An investigation

Author

Amin, Saddam Al, Mukta, Md Saddam Hossain, Saikat, Md Sezan Mahmud, Hossain, Md Ismail, Islam, Md Adnanul, Ahmed, Mohiuddin, Azam, Sami, Amin, Saddam Al, Mukta, Md Saddam Hossain, Saikat, Md Sezan Mahmud, Hossain, Md Ismail, Islam, Md Adnanul, Ahmed, Mohiuddin, and Azam, Sami

Abstract

The opioid crisis has led to an increased number of drug overdoses in recent years. Several approaches have been established to predict opioid prescription by health practitioners. However, due to the complex nature of the problem, the accuracy of such methods is not yet satisfactory. Dependable and reliable classification of opioid dependent patients from well-grounded data sources is essential. Majority of the previous studies do not focus on the users’ mental health association for opioid intake classification. These studies do not also employ the latest deep learning based techniques such as attention and knowledge distillation mechanism to find better insights. This paper investigates the opioid classification problem by using machine learning and deep learning based techniques. We used structured and unstructured data from the MIMIC-III database to identify intentional and unintentional intake of opioid drugs. We selected 455 patient instances and used traditional machine learning and deep learning to predict intentional and accidental users. We obtained 95 % and 64 % test accuracy to predict the intentional and accidental users from the structured and unstructured datasets, respectively. We also achieve a distilled knowledge based test accuracy of 76.44 % from the integrated above two models. Our research includes an ablation analysis and new insights related to opioid patients are extracted.

Published
2022

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